Gaussian Mixture Modeling

However, the commonly used Gaussian mixture model and modeling based on kernel estimation both have their own shortcomings.

而通常所采用的混合高斯模型和基于核估计的方法具有各自的缺点。

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Thus, the paper focuses on the following aspects. First, in the extraction of the target vehicle, the paper analyzes some commonly used methods which have Multi-frame averaging method, continuous frame difference method, optical flow method, Gaussian mixture modeling approach.

因而论文着重研究了以下几个方面。首先论文在对目标车辆提取方面，分析了一些常用的方法有多帧平均法、连续帧差法、光流法、高斯混合建模法等。

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The improved algorithm replaces the tone reverse projection image of the target vehicle with the target vehicle binary image obtained by the Gaussian mixture modeling method.

改进的算法是利用混合高斯建模法得到的目标车辆二值图像，替换目标车辆跟踪的色调反向投影图像。

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This article optimizes the Gaussian Mixture background modeling algorithm through in-depth study of it, improving the speed of updates, and almost does not change the accuracy.

通过对混过高斯背景建模算法的研究，优化了背景更新算法，在几乎不改变准确率的前提下提高了背景更新的速度，为进一步的操作提供了较好的基础。

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Scalable Parameter Learning Algorithm for Gaussian Mixture Modeling

高斯混合模型的数据尺度可压缩参数学习算法

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Structural risk minimization principle based gaussian mixture modeling

基于结构风险最小化准则的高斯混合模型的参数估计

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We perform unsupervised classification to hyperspectral image by Gaussian mixture modeling, estimate the parameters of the mixture model using the expectation maximization ( EM) algorithm in merged operations to data after PCA linear projection, and select the number of components according to the proposed criterion.

分类过程中，论文采用期望最大化（Expectation maximization）算法在合并的策略下对PCA变换后的数据求解混合模型，并应用所提出的准则进行模型选择从而确定待分类的类别数。

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Further programming of the above four algorithms, simulation results show that Gaussian mixture modeling algorithm is better and more conducive to the transport vehicle segmentation.

进一步通过编程实现上述四种算法，仿真实验结果表明，混合高斯建模算法的分割效果更好，更有利于交通车辆分割。

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For all of the background modeling methods, the Gaussian mixture modeling is considered as a good method with high performance in both detection capability and adaptability. Nevertheless, it is difficult to implemented in real time for its huge computational complexity.

在背景建模各方法中，混合高斯建模方法是公认的检测效果和适应性都较好的方法，但其所需计算量巨大，难以实时实现。

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